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Data Science Initiative 1 Data Science Initiative 1 3 credits in data and computational science, 1 credit in societal implications and opportunities, Data Science Initiative 1 elective credit to be drawn from a wide range of focused applications or deeper theoretical exploration, and 1 credit capstone experience. Director We also offer an option as a 5-th Year Master's Program if you are an Sohini Ramachandran undergraduate at Brown. This allows you to substitute maximally 2 credits Direfctor of Graduate Studies with courses you have already taken. Samuel S. Watson Master of Science in Data Science Brown University's Data Science Initiative serves as a campus hub Semester I for research and education in data science. Engaging partners across DATA 1010 Probability, Statistics, and Machine 2 campus and beyond, the DSI 's mission is to facilitate and conduct both Learning domain-driven and fundamental research in data science, increase data DATA 1030 Hands-on Data Science 1 fluency and educate the next generation of data scientists, and ultimately DATA 1050 Data Engineering 1 explore the impact of the data revolution on culture, society, and social justice. We envision our role in the university and beyond as something to Semester II build over time, with the flexibility to meet the changing needs of Brown’s DATA 2020 Statistical Learning 1 students and research community. DATA 2040 Deep Learning and Special Topics in Data 1 The Master’s Program in Data Science (Master of Science, Science ScM) prepares students from a wide range of disciplinary backgrounds DATA 2080 Data and Society 1 for distinctive careers in data science. Rooted in a research An appropriate 1000-level or 2000-level course to be determined 1 collaboration between four strong academic departments (Applied by the student and approved by the program advisor. Possible Mathematics, Biostatistics, Computer Science, and Mathematics), the courses could range from advanced mathematical methods to Master's Program offers a unique and rigorous education for people very specific applications of data science. building careers in data science and/or big data management. Summer For additional information, please visit the initiative's website: http:// DATA 2050 Data Science Practicum 1 1 dsi.brown.edu/ Total Credits 9 1 Data Science Graduate Program For their capstone experience, students will work on a project with real data, potentially in any one of the areas covered by the elective Master of Science in Data Science course. A faculty member from one of the four departments will The Data Science Initiative at Brown offers a new master's program (ScM) oversee the capstone course, although each student may collaborate that will prepare students from a wide range of disciplinary backgrounds with an additional faculty member, postdoc, or industry partner on his/ for distinctive careers in Data Science. Rooted in a research collaboration her project. among four very strong academic departments (Applied Mathematics, For more information on admission and program requirements, please visit Biostatistics, Computer Science, and Mathematics), the master's program the following website: will offer a rigorous, distinctive, and attractive education for people building careers in Data Science and/or in Big Data Management. The program's https://www.brown.edu/academics/gradschool/programs/data-science main goal is to provide a fundamental understanding of the methods (https://www.brown.edu/academics/gradschool/programs/data-science/) and algorithms of Data Science. Such an understanding will be achieved Courses through a study of relevant topics in mathematics, statistics and computer science, including machine learning, data mining, security and privacy, DATA 0080. Data, Ethics and Society. visualization, and data management. The program will also provide A course on the social, political, and philosophical issues raised by experience in important, frontline data-science problems in a variety the theory and practice of data science. Explores how data science is of fields, and introduce students to ethical and societal considerations transforming not only our sense of science and scientific knowledge, surrounding data science and its applications. but our sense of ourselves and our communities and our commitments concerning human affairs and institutions generally. Students will examine The program's course structure, including the capstone experience, will the field of data science in light of perspectives provided by the philosophy ensure that the students meet the goals of acquiring and integrating of science and technology, the sociology of knowledge, and science foundational knowledge for data science, applying this understanding in studies, and explore the consequences of data science for life in the first relation to specific problems, and appreciating the broader ramifications half of the 21st century. Fulfills requirement for Certificate in Data Fluency of data-driven approaches to human activity. Moreover, our strong Fall DATA0080 S01 17954 TTh 10:30-11:50(13) (D. Hurley) industry partnerships will help you better learn about industry's needs and directions, and will expose you to novel and unique opportunities. DATA 0200. Data Science Fluency. In addition, several professors from all across the different department's As data science becomes more visible, are you curious about its unique groups work closely with industry (regional and beyond) and the amalgamation of computer programming, statistics, and visualizing government, so you will be able to sharpen your skills here on problems or storytelling? Are you wondering how these areas fit together and that bring research ideas and methods to bear on problems of practical what a data scientist does? This course offers all students regardless value. of background the opportunity for hands-on data science experience, The program will be conducted over one academic year plus one summer, following a data science process from an initial research question, through with the option for an additional pre-program summer for students who data analysis, to the storytelling of the data. Along the way, you will learn lack one or more of the basic prerequisites. The regular program includes about the ethical considerations of working with data, and become more two semesters of coursework and a one-summer (5- 10 week) capstone aware of societal impacts of data science. Course does not count toward project focused on data analysis in a particular application area. CS concentration requirements. Spr DATA0200 S01 26425 TTh 2:30-3:50(11) (L. Clark) There are nine credits unites required to pass the program: four in each of the academic year semesters, and one (the capstone experience) in the summer. The nine credit-units divide as follows: 3 credits in mathematical and statistical foundations, Data Science Initiative 1 2 Data Science Initiative DATA 1010. Probability, Statistics, and Machine Learning. DATA 2020. Statistical Learning. An introduction to the mathematical methods of data science through A modern introduction to inferential methods for regression analysis and a combination of computational exploration, visualization, and theory. statistical learning, with an emphasis on application in practical settings Students will learn scientific computing basics, topics in numerical linear in the context of learning relationships from observed data. Topics will algebra, mathematical probability (probability spaces, expectation, include basics of linear regression, variable selection and dimension conditioning, common distributions, law of large numbers and the central reduction, and approaches to nonlinear regression. Extensions to other limit theorem), statistics (point estimation, confidence intervals, hypothesis data structures such as longitudinal data and the fundamentals of causal testing, maximum likelihood estimation, density estimation, bootstrapping, inference will also be introduced. and cross-validation), and machine learning (regression, classification, and Spr DATA2020 S01 25713 TTh 10:30-11:50(09) (R. DeVito) dimensionality reduction, including neural networks, principal component analysis, and unsupervised learning). DATA 2040. Deep Learning and Special Topics in Data Science. A hands-on introduction to neural networks, reinforcement learning, Fall DATA1010 S01 18019 MWF 11:00-11:50(14) (S. Watson) and related topics. Students will learn the theory of neural networks, Fall DATA1010 S01 18019 MWF 10:00-10:50(14) (S. Watson) including common optimization methods, activation and loss functions, DATA 1030. Hands-on Data Science. regularization methods, and architectures. Topics include model Develops all aspects of the machine learning pipeline: data acquisition and interpretability, connections to other machine learning models, and cleaning, handling missing data, exploratory data analysis, visualization, computational considerations. Students will analyze a variety of real-world feature engineering, modeling, interpretation, presentation in the context problems and data types, including image and natural language data. of real-world datasets. Fundamental considerations for data analysis Spr DATA2040 S01 26298 TTh 1:00-2:20(08) ’To Be Arranged' are emphasized (the bias-variance tradeoff, training, validation, testing). DATA 2050. Data Science Practicum. Classical models and techniques for classification and regression are The capstone experience is a hands-on thesis project that entails an in- included (linear and logistic regression with regularization,
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